One of methods for detecting an abnormality in a control device is model-based anomaly detection. Model-based anomaly detection refers to making a model of a process to be monitored in advance, taking a difference between data observed as an output of a process for a given input and an output predicted from the model, and outputting the degree of abnormality of a control device on the basis of a value of the difference. Model-based anomaly detection includes a model learning function of constructing an approximate model having an unknown parameter and estimating the unknown parameter from actual performance data without an abnormality.
To perform anomaly detection on a model basis, it is often necessary to define a device characteristic used in a process to be monitored, such as a Cv characteristic (flow characteristic) with respect to damper position, as a parametric function expression and construct an approximate model.
However, a device characteristic is rarely given as a mathematical expression, and the cost of providing a high-accuracy approximate model each time a new device is introduced. Additionally, each device has an individual difference and suffers from the problem of being biased and deviating from a characteristic graph.
For example, in Japanese Patent No. 3254624, a sliding characteristic as an approximate model for detecting an abnormality in stick-slip of a control valve is defined by x″=A* x′+B, and the parameters “A” and “B” are estimated from actual performance data.
According to an aspect of the present invention, there is provided an anomaly detecting apparatus which allows high-accuracy detection of an abnormality in a control device having an individual difference and an unknown characteristic.
An embodiment of the present invention allows high-accuracy detection of an abnormality in a control device having an individual difference and an unknown characteristic.